This paper is concerned with the guaranteed cost control problem for a class of Markov jump discrete-time neural networks (NNs) with event-triggered mechanism, asynchronous jumping, and fading channels. The Markov jump NNs are introduced to be close to reality, where the modes of the NNs and guaranteed cost controller are determined by two mutually independent Markov chains. The asynchronous phenomenon is considered, which increases the difficulty of designing required mode-dependent controller. The event-triggered mechanism is designed by comparing the relative measurement error with the last triggered state at the process of data transmission, which is used to eliminate dispensable transmission and reduce the networked energy consumption. In addition, the signal fading is considered for the effect of signal reflection and shadow in wireless networks, which is modeled by the novel Rice fading models. Some novel sufficient conditions are obtained to guarantee that the closed-loop system reaches a specified cost value under the designed jumping state feedback control law in terms of linear matrix inequalities. Finally, some simulation results are provided to illustrate the effectiveness of the proposed method.

Event-Triggered Asynchronous Guaranteed Cost Control for Markov Jump Discrete-Time Neural Networks With Distributed Delay and Channel Fading

This paper studies the fuzzy adaptive distributed event-based control scheme for a class of uncertain nonlinear multiagent systems in strict feedback form. A novel distributed adaptive event-trigger condition and event-triggered controller are designed simultaneously. The main advantage with respect to event-sampling states is that the controllers were updated in an aperiodic manner at the event sampled instants, saving the computation resource and transmission load. Simulation results have shown satisfactory tracking performance of the developed control scheme.

Fuzzy Adaptive Distributed Event-Triggered Consensus Control of Uncertain Nonlinear Multiagent Systems

This paper is concerned with the target tracking problem over a filtering network with dynamic cluster and data fusion. A novel distributed consensus-based adaptive Kalman estimation is developed to track a linear moving target. Both optimal filtering gain and average disagreement of the estimates are considered in the filter design. In order to estimate the states of the target more precisely, an optimal Kalman gain is obtained by minimizing the mean-squared estimation error. An adaptive consensus factor is employed to adjust the optimal gain as well as to acquire a better filtering performance. In the filter’s information exchange, dynamic cluster selection and two-stage hierarchical fusion structure are employed to get more accurate estimation. At the first stage, every sensor collects information from its neighbors and runs the Kalman estimation algorithm to obtain a local estimate of system states. At the second stage, each local sensor sends its estimate to the cluster head to get a fused estimation. Finally, an illustrative example is presented to validate the effectiveness of the proposed scheme.

Adaptive Consensus-Based Distributed Target Tracking With Dynamic Cluster in Sensor Networks

In this paper, we study the exponential stabilization problem for continuous-time Takagi–Sugeno fuzzy systems subject to aperiodic sampling. By aiming to transmission reduction, an appropriate aperiodic event-triggered communication scheme with adaptive mechanism is put forward, which covers the existing periodic mechanisms as special cases. For the sake of reduction in design conservativeness, both the available information of sampling behavior and threshold error are fully acquired by constructing a novel time-dependent Lyapunov functional. Then, a new exponential stability criterion is presented to establish the quantitative relationship among the adaptive adjusted event threshold, the decay rate, the upper bound, and the lower bound of variable sampling period, simultaneously. By resorting to a matrix transformation, the corresponding stabilization criterion is further derived by which the sampled-data controller can be obtained. Finally, two illustrative examples are provided to demonstrate the virtue and applicability of proposed design method.

Exponential Stabilization of Takagi–Sugeno Fuzzy Systems With Aperiodic Sampling: An Aperiodic Adaptive Event-Triggered Method

The $\boldsymbol {H_{\infty }}$ output tracking control problem of the networked control systems (NCSs) under an adaptively adjusted event-triggered scheme is investigated in this paper. Firstly, a novel adaptively adjusted event-triggered scheme developed in the NCSs with stochastic sensor faults is proposed to choose the necessary packets of sampled data to be transmitted through the networks. Then, the considered system is described as a time-delay system with a delay-distribution for investigation. Based on the established model, a novel stability criterion and the state-feedback controller design with a desired performance of $\boldsymbol {H_{\infty }}$ output tracking control for the systems are both derived by using Lyapunov functional. An algorithm is also presented to explain the process of adaptively adjusted event-triggered scheme method. Finally, a satellite tracking case is provided to demonstrate the effectiveness of the proposed approach.

$H_{\infty}$ Output Tracking Control for Networked Systems With Adaptively Adjusted Event-Triggered Scheme

In this paper, the distributed ${H_{\infty }}$ state estimation problem is investigated for a class of filtering networks with time-varying switching topologies and packet losses. In the filter design, the time-varying switching topologies, partial information exchange between filters, the packet losses in transmission from the neighbor filters and the channel noises are simultaneously considered. The considered topology evolves not only over time, but also by event switches which are assumed to be subjects to a nonhomogeneous Markov chain, and its probability transition matrix is time-varying. Some novel sufficient conditions are obtained for ensuring the exponential stability in mean square and the switching topology-dependent filters are derived such that an optimal ${H_{\infty }}$ disturbance rejection attenuation level can be guaranteed for the estimation disagreement of the filtering network. Finally, simulation examples are provided to demonstrate the effectiveness of the theoretical results.

Distributed $H_\infty$ State Estimation for a Class of Filtering Networks With Time-Varying Switching Topologies and Packet Losses

This paper investigates the problem of robustly globally uniformly exponential stability and L 2 control for a class of switched systems with time-varying delay by using mode-dependent average dwell time (MDADT) approach. The MDADT approach is more applicable in practice than the average dwell time method in which each mode in the underlying system has its own average dwell time. Firstly, some sufficient conditions for robustly exponential stability is given by using the MDADT approach and piecewise Lyapunov function. Secondly, the L 2 -gain of the switched system with the external disturbance is analyzed. Then, the state feedback controller gain is obtained to ensure the exponential stability of the switched system. Finally, a numerical example is provided to illustrate the effectiveness of the theoretical results.

Qian Yang

Papers

Distributed $H_\infty$ State Estimation for a Class of Filtering Networks With Time-Varying Switching Topologies and Packet Losses

Exponential stability and L 2 control for delayed switched systems with mode-dependent average dwell time